Download A model for evaluating the `habitat potential` of a landscape for

A model for evaluating the 'habitat potential' of a landscape for
capercaillie Tetrao urogallus: a tool for conservation planning
Veronika Braunisch & Rudi Suchant
Braunisch, V. & Suchant, R. 2007: A model for evaluating the 'habitat
potential' of a landscape for capercaillie Tetrao urogallus: a tool for conservation planning. - Wildl. Biol. 13 (Suppl. 1): 21-33.
Most habitat models developed for defining priority conservation sites
target areas currently exhibiting suitable habitat conditions. For species
whose habitats have been altered by land use practices, these models may
fail to identify sites with the potential of producing suitable habitats, if
management practices were modified. Using capercaillie Tetrao urogallus
as an example, we propose a model for evaluating the potential of ecological conditions at the landscape level to provide suitable habitat at the
local scale. Initially, we evaluated the influence of selected landscape
parameters on the structural characteristics of vegetation relevant to capercaillie. Then we used capercaillie presence data and an ecological niche
factor analysis (ENFA) to identify landscape and land use variables relevant to capercaillie habitat selection. We also studied the effect of scale
on predictive model quality. Despite high variance, correlations between
landscape variables and forest structure were detected. The greatest influence on forest structure was recorded for climate and soil conditions,
which were also found to be the best predictors of capercaillie habitat
selection in the ENFA. The final model, retaining only two landscape
variables (soil conditions and days with snow) and three land use variables (proportion of forest, distance to roads and forest-agricultural borders), explained a high degree of capercaillie habitat selection, even before
considering patch size and connectivity. By restricting the analyses to
areas with stable subpopulations and a set of relatively stable landscape
variables capable of explaining habitat quality at a local scale, we were
able to identify areas with long-term relevance to conservation of capercaillie.
Key words: capercaillie, conservation, ecological niche factor analysis,
habitat model, 'habitat potential', landscape scale, Tetrao urogallus
Veronika Braunisch & Rudi Suchant, Forstliche Versuchs- und Forschungsanstalt Baden-Württemberg, Wonnhaldestr. 4, 79100 Freiburg im Breisgau, Germany - e-mail address:[email protected] (Veronika Braunisch); [email protected] (Rudi Suchant)
Corresponding author: Veronika Braunisch
Given the limited area within central Europe suitable as wildlife habitat, and the resultant need to
integrate wildlife ecology concerns into landscape
planning, it is important to ask: "Which areas are
relevant to an endangered species?" Yet, how
should priority areas for conservation measures
be defined? Considering only areas where an endangered species is currently present is obviously not
sufficient, although this is still common practice in
nature conservation and landscape planning. The
development of increasingly powerful geographic
information systems and the growing availability
21
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:53:55
21
of digital landscape data in recent years have promoted the creation of large-scale habitat models for
the identification of species-relevant sites (e.g. Mladenoff & Sickley 1998, Kobler & Adamic 2000,
Schadt et al. 2002, Graf et al. 2005, Zajec et al.
2005). Most of these models are based on a comparison between currently inhabited and uninhabited
areas and the identification of relevant habitat parameters, followed by the delimitation of potentially suitable habitats. However, for declining species
whose habitats are massively influenced by anthropogenic land use and silvicultural practices, such
approaches may be of limited use, as they analyse
and extrapolate only a momentary state in an ongoing dynamic process. For example, no decision
can be made as to whether the areas with currently
suitable habitat conditions are the outcome of past
or present management or whether the habitat conditions are a natural result of the prevailing ecological conditions (e.g. climate, soil conditions and topography) and thus have a high potential to remain
suitable in the future.
Because of its broad spatial and specific habitat requirements (e.g. Klaus et al. 1989, Storch 1993a,b,
1995), and owing to its function as an umbrella species
(e.g.Suter et al. 2002), the capercaillie Tetraourogallus
is a popular model species for the analysis of specieshabitat interrelationships. This tetraonid is indicative
of structurally rich and continuous boreal and montane forest habitats (Scherzinger 1989, 1991, Boag &
Rolstad 1991, Storch 1993b, 1995, Cas & Adamic
1998, Simberloff 1998). Within these forested habitats, the geographic distribution of the capercaillie
corresponds largely to the spatial pattern of forest
fragmentation and the prevailing climatic conditions.
The Black Forest in southwestern Germany
holds the largest capercaillie population in Central
Europe outside the Alps. Up until the end of the
19th century, the capercaillie was present down into
the lowlands. There, secondary habitats had been
created by intensive use of forests, including grazing, which opened the forests, and raking of litter
for use in stables, which removed nutrients (especially nitrogen) from the soil and led to a change in
tree species composition (Suchant 2002, Gatter
2004). Partly as a result of the deterioration of these
secondary habitats (Gatter 2004), the capercaillie
population has continually declined over the last
100 years and is now limited to higher altitudes
(Suchant & Braunisch 2004).
The habitat requirements of capercaillie have
been investigated in the past, primarily at the local
level (e.g. Gjerde et al. 1989, Rolstad 1989, Helle et
al. 1990, Storch 1993a,b, 1995, Schroth 1995,
Wegge et al. 1995). In view of the capercaillie’s spatial requirements, variables occurring at the landscape level have recently been considered (Ménoni
1994, Sachot 2002, Storch 2002, Suchant 2002,
Graf et al. 2005). The purpose of our study was to
identify the ecological conditions at a landscape
scale that define the preconditions for the development of suitable capercaillie habitat at a local scale.
Therefore, we focused on indirect variables (Austin
& Smith 1989, Austin 2002), which may replace
a combination of different resources in a simple
manner and which are more stable and less susceptible to anthropogenic influences than resource
variables (Guisan et al. 1999). We hypothesised that
a habitat model restricted to a small set of such
landscape variables, analysed in areas where subpopulations of capercaillie were stable, would allow
us to identify areas with a long-term potential for
producing and maintaining suitable habitat. We
chose different methodological steps to answer the
following questions: 1) which landscape variables
support the development of suitable capercaillie
habitat at a local scale, 2) which landscape and land
use variables are relevant to habitat selection by
capercaillie and on which spatial scale must they
be considered, and 3) to what extent can a model,
based on the resultant variables, explain the distribution of capercaillie?
Material and methods
Study area
The study area encompasses the Black Forest, a low
forested mountain range in southwestern Germany
covering ca 7,000 km2. The elevation ranges within
120-1,493 m a.s.l. Capercaillie are currently present
on about 510 km2 at the higher altitudes, with 258
cocks counted in 2003 (Braunisch & Suchant 2006).
We distinguished three main capercaillie regions:
the southern, northern and eastern Black Forest
subregions.
Capercaillie data
The capercaillie has been continuously monitored
in the Black Forest since 1988. Every five years
its distribution is mapped, using all direct and indirect evidence of capercaillie presence provided by
foresters, hunters, ornithologists and our research
personnel. Furthermore, the locations of lekking
22
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:53:56
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
22
Table 1. Landscape and land use variables (landscape scale) used in modelling capercaillie 'habitat potential'. Among the reasons for
omitting variables from the reduced model (righternmost column), 'Correl.' means that correlation r was $ 0.7 and 'Insufficient contr.'
that the contribution to marginality and specialisation was , 0.2.
Variable category
Variable description
Code
Unit
Data source
Reason for omission
from reduced model
Landscape variables
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Climate
Number of days per year with snow
snowd
days
DEM
Retained
cover . 10 cm
Average annual temperature
tempyear uC
German met. service
Correl. with snowd
Duration of the vegetation period
vegdur
days
German met. service
Correl. with snowd
(temperature . 10 uC)
Potential sunshine duration
soldur
hours
DEM, SAGA-gis model Insufficient contr.
(April - September)
Potential solar radiation
DEM, SAGA-gis model Insufficient contr.
solrad
kWh/m2
(April - September)
------------------------------ -------------------------------------------------------------------------- --------------------------------------------------------- ----------------------------Soil conditions
Soil conditions, evaluated according
scval
index (1-15)
Soil condition database Retained
to their potential to support suitable
forest types
------------------------------ -------------------------------------------------------------------------- --------------------------------------------------------- ----------------------------Topography
Slope
slope
degree
DEM
Ecological plausibility
& insufficient contr.
Topographic exposure
topex
(+/2) degree
DEM
Insufficient contr.
+ 1000
------------------------------ -------------------------------------------------------------------------- --------------------------------------------------------- ----------------------------Land-use variables
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Forest
Proportion of forest
foall
% of area
Landsat 5
Retained
Proportion of coniferous and mixed forest
fcomi
% of area
Landsat 5
Correl. with foall
Proportion of forest-agriculture border
agfor
% of area
Landsat 5
Retained
area (200 m width)
------------------------------ -------------------------------------------------------------------------- --------------------------------------------------------- ----------------------------Agriculture
Proportion of agriculture
agall
% of area
Landsat 5
Correl. with foall
Distance to agriculture
agdist
m
Landsat 5
Correl. with agall
------------------------------ -------------------------------------------------------------------------- --------------------------------------------------------- ----------------------------Settlements
Proportion of settlements and urban
urb
% of area
Landsat 5
Correl. with stdist
area
Distance to settlements and urban area
urbdist
m
Landsat 5
Correl. with stdist
------------------------------ -------------------------------------------------------------------------- --------------------------------------------------------- ----------------------------Linear infrastructure Proportion of area influenced by roads
stall
% of area
ATKIS
Correl. with stdist
(plus 100 m buffer)
% of area*
ATKIS/Ministry of
Correl. with stdist
Proportion of roads (plus 100 m buffer)
sttra
traffic-index
Traffic
weighted according to average traffic/day
Distance to roads
stdist
m
ATKIS
Retained
places are mapped and the number of cocks
counted annually (Braunisch & Suchant 2006).
For our analyses, we randomly sampled 1,600 presence points, with at least 300 m between points to
reduce bias from spatial autocorrelation. The proportion of records selected from each subregion
(south, north and east) corresponded to the mean
proportion of cocks counted in each area. In addition, to restrict the landscape analyses to areas with
'stable' subpopulations of birds, we included only
records from patches that had been consecutively
mapped as 'inhabited' since 1988.
Landscape and land use variables
The landscape-scale variables tested in the model
(Table 1) were subdivided into two categories:
'landscape' and 'land use' variables. 'Landscape'
variables are environmental factors that are expected to affect the composition and structure of
forests and other vegetation. They therefore define
the natural potential of the landscape for the development of suitable habitat. 'Land use' variables
in contrast, describe the current distribution of forest and human land-use features and, therefore,
may define the area that is currently available for
use by capercaillie.
The landscape variables included characteristics
of climate, soil conditions and topography. As the
restriction of central European capercaillie populations to the higher altitudes of montane regions
(Klaus et al. 1989, Storch 2000) is a consequence
of climate, rather than altitude, we compared three
climate variables correlated with altitude: 1) 'average annual temperature', 2) 'duration of the vege23
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:53:56
23
tation period' and 3) 'number of days with .10 cm
snow cover', calculated according to Schneider &
Schönbein (2003). In addition, 'potential sunshine
duration' and 'potential solar radiation' during the
vegetation period (April-September) were modelled
according to Böhner et al. (1997).
Analyses of soil conditions were based on a soil
condition database. Soil texture, soil type, humus
type, nutrient status and hydrological regime were
evaluated separately with respect to their potential
to support selected capercaillie habitat structures, including ground vegetation dominated by bilberry
Vaccinium myrtillus, nutrient-poor forest types dominated by conifers or pines, bogs and wet forests. The
variables were then aggregated into a soil condition
index using an expert model (V. Braunisch, M. Wiebel, H.G. Michiels & R. Suchant, unpubl. data).
Topographic exposure was determined using the
topex-index (Wilson 1984), which qualifies a point’s
position relative to the surrounding terrain. The
topex-to-distance index employed here was calculated as the sum of angles to the ground within
a fixed distance, measured for each of the eight cardinal directions (Mitchell et al. 2001). A distance of
2,000 m was chosen because Hannah et al. (1995)
found this topex to be strongly correlated with the
probability of windfall events, which favour open
forest structures.
Landusevariables describetheavailabilityandspatial distribution of existing land use features (forest,
forest fragmentation and agricultural areas), including the distribution of possible sources of disturbance
(settlements and linear infrastructures). A distinction
was made between 'forest' in general, which grouped
all available forest categories, and 'coniferous and
mixed forest', which excludedpurely deciduous forest.
As a measure of forest fragmentation, we calculated a 200-m wide forest-agricultural border zone,
which included a 100-m buffer on either side of the
forest edge. As intensive agriculture (arable fields,
orchards and grassland) is very rare in the Black
Forest and the map of 'intensive agriculture' would
have neglected the minimum criteria for statistical
normality, it was pooled with non-intensively used
grassland and pastures.
To the potential sources of disturbance, i.e. settlements and linear infrastructures, we added a 'disturbance' buffer of 100 m. Two different maps were
constructed for linear infrastructures. On the first
map, depicting the fragmentation effect of roads,
we pooled all road categories (e.g. main roads,
county roads and rural roads) and railways. On
the second map, highlighting the disturbance effect
of roads, the different road categories were weighted according to average traffic density.
We prepared raster maps with a 30 3 30 m grid
for all variables. As the two climate variables 'average annual temperature' and 'duration of the vegetation period' were only available at a resolution of
1 km2, we produced an additional map depicting
'number of days with .10 cm snow cover' at this
1-km2 resolution for a separate comparison. To determine the spatial scale at which a variable performed best, we calculated the mean value for each
variable within circular moving windows of 10, 100,
500 and 1,000 ha. These scales correspond to the
size of an average forest stand (10 ha), the size of
a small (100 ha) and large (500 ha) individual capercaillie home range, and to the average size of an
occupied habitat patch in the study area (1,000 ha).
As multinormality was required, all variables were
normalised using the Box-Cox standardising algorithm (Box & Cox 1964, Sokal & Rohlf 1981). Maps
were prepared in ArcView (ESRI 1996) and converted to IDRISI.
Habitat structure data
Data on habitat structure at a local scale were taken
from the national forest inventory. The sampling
strategy was based on a 2 3 2 km Gauss-Krüger
grid. The corner of each grid cell represented the
centre of a 150 3 150 m square, the corners of which
in turn each marked the centre of a sample plot. A
total of 4,308 sample plots were distributed over the
afforested parts of the study area, with selected habitat structure variables mapped within different radii around the sample plot centre (Table 2). The
influence of landscape variables on vegetation
structure was tested using multiple linear regression
(STATISTICA, StatSoft 1999).
Statistical methods
Modelling approach
Multivariate approaches to modelling habitat suitability or to predicting species presence (e.g. logistic
regression) usually require presence-absence data.
The ecological niche factor analysis (ENFA; Hirzel
et al. 2002), based on Hutchinson’s (1957) concept
of the ecological niche, compares the conditions of
sites with proved species presence against the conditions of the whole study area, requiring only presence data. All predictor variables included in the
model are transformed to an equal number of un-
24
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:53:57
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
24
Table 2. Habitat structure variables relevant for capercaillie at local scale tested for correlation with the landscape variables listed in
Table 1 and the size of the sample plots in which they were mapped.
Category
Forest structure variable
Unit
Sample unit
Forest type
Conifer dominated forest types
Yes/no
Forest stand containing the centre of the sample plot
Pine dominated forest types
Yes/no
Forest stand containing the centre of the sample plot
---------------------------------- ------------------------------------------------- ---------------------------------------------------------------------------------------------------------Forest age
Old forest
(years . 70) yes/no
Forest stand containing the centre of the sample plot
---------------------------------- ------------------------------------------------- ---------------------------------------------------------------------------------------------------------Forest structure
Density (related to ideal crown
Index
Sample plot (r 5 25 m)
projection and ha)
Sample plot (r 5 25 m)
Increment (10 years)
m3/year ha
Windfall (10 years)
Yes/no
Sample plot (r 5 25 m)
---------------------------------- ------------------------------------------------- ---------------------------------------------------------------------------------------------------------Ground vegetation
Ground vegetation coverage
Index
Sample plot (r 5 10 m)
Bilberry coverage
% coverage (4 classes)
Sample plot (r 5 10 m)
correlated and standardised factors. The first factor
explains the species’ marginality (M 5 the difference between the average conditions within areas
with species presence and those in the entire study
area), which defines the location of the species’
niche in relation to the range of available conditions. It also explains part of the specialisation
(S 5 the ratio between the standard deviation
(SD) of the conditions in the entire study area and
the SD of the conditions where the species is present), which defines the niche breadth. The subsequent factors explain the rest of the specialisation.
Variable and scale selection
Initially an ENFA was performed including all
variables at all spatial scales. Then we calculated
a multi-scale model including all variables, each at
the scale where it performed best and compared this
with four single-scale models, including all variables at the same scale (10, 100, 500 and 1,000 ha).
To obtain a simple final model without losing too
much information, we selected the best of the aforementioned models and reduced the initial set of
variables using the following criteria. A variable
was only included in the final model if 1) it made
a sufficient contribution to marginality or specialisation (. 0.2), 2) it showed the same algebraic sign
in the coefficient value of the marginality factor (indicating avoidance or preference) at all spatial
scales, and 3) it was ecologically plausible. In addition, if bivariate correlation between any two remaining variables exceeded a threshold of 0.7, the
variable with the lower contribution to specialisation and marginality was discarded.
Landscape model of 'habitat potential'
We defined 'habitat potential' as the capacity of
landscape conditions to give rise to suitable capercaillie habitat. Some areas of the landscape may
already exhibit suitable habitat, others might be
capable of producing such habitats, depending on
management practices and natural evolution of the
vegetation. We calculated an index to 'habitat potential' using the 'area-adjusted median algorithm
with an extreme optimum' for the marginality part
of the first factor. This modification of the median
algorithm (Hirzel & Arlettaz 2003a) was especially
developed for species existing at the edge of their
natural distribution range (Braunisch et al. submitted). The number of significant factors retained
for the calculation of 'habitat potential' was chosen
according to the broken-stick model (MacArthur
1960, Hirzel et al. 2002). Indices of 'habitat potential' ranged from 0 (unsuitable for capercaillie)
to 100, with low values representing suboptimal
areas.
Model validation
For model evaluation we applied a 10-fold areaadjusted frequency cross validation (AAFCV;
Fielding & Bell 1997). The model quality was quantified using the continuous Boyce index (Hirzel et
al. 2006). In addition, we calculated the 'regular'
Boyce index (Boyce et al. 2002), the absolute validation index (AVI), which calculates the proportion of evaluation points occurring in the predicted
core habitats, and the contrast validation index
(CVI), which compares this value with the value
that could be expected from a random model (Hirzel & Arlettaz 2003b, Hirzel et al. 2004).
Results
Landscape variables - habitat structure
Although parameter values varied greatly between
sample plots, multiple regression analysis showed
significant correlations between landscape condi25
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:53:57
25
26
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:53:58
Low values indicate high topographic exposure.
1
Unit
Level P-value of multiple R
0.31***
0.23***
0.10***
0.12***
0.17***
0.16***
0.55***
0.23***
---------------------------------------- ------------- ------------------------------------------------------ ------------------------------------------------------------------------------ -----------------------------------------------------------------------------------Days with snow .10 cm
Day
0.246***
-0.121***
-0.091***
-0.066***
0.311***
0.038*
-0.058**
0.047**
0.057**
0.066*
-0.076***
Potential solar radiation
kWh/m2
Potential sun-shine duration
Hour
0.096***
Soil conditions
Index
0.097***
0.217***
0.070***
-0.077***
-0.104***
0.084***
0.342***
0.082***
Topographic exposure1
Index
-0.109***
0.066*
0.080*
-0.089***
0.047
0.072**
Slope
Degree
-0.103***
-0.088**
-0.121***
-0.218***
Forest structure
Vegetation coverage
--------------------------------------------------------------------------- ----------------------------------------------Density
Growth capacity Frequency of windthrow
Bilberry
Ground vegetation
---------------- ---------------------------------------------------- ---------------- ---------------------------Number of
Increment m3/
year ha
sample plots
% coverage
Index
Index
Variable selection, marginality and specialisation
The contribution of the different variables to marginality and specialisation for the 10-ha single-scale
model is shown in Table 4. The marginality factor
also explains the highest degree (28.9%) of the specialisation. This indicates a quite narrow niche
breadth for the variables that most differentiate
capercaillie habitats from the average conditions
in the study area.
Of the landscape variables, snowd (days with
snow cover) exhibited the highest contribution to
both marginality and specialisation. In the separate
comparison (only one significant factor, therefore
M 5 S) it also performed better (coefficient value:
0.632) than the two other altitude-correlated climate variables vegdur (0.569) and tempyear
(0.526), which were therefore excluded from further
analyses. The second most important variable was
scval (soil conditions). However, although capercaillie areas are characterised by a much higher soil
condition index than average, the degree of specialisation is rather moderate. The other landscape
variables (topex, slope, soldur and solrad) revealed
only weak contributions to both marginality and
specialisation.
Concerning the land use variables, capercaillie
shows highest marginality in relation to distances
to roads (stdist), agricultural areas (agdist) and settlements (urbdist), but it is most specialised in the
selection of areas with a high abundance of forest
(foall) and in the avoidance of agricultural areas
(agall). The similar absolute values for these two
variables are not surprising, as they are highly neg-
Forest type
Forest age
--------------------------------------------------- -------------------------------Conifer-dominated
Pine-dominated Proportion of old forest
-------------------------- --------------------- -------------------------------Number of
Number of
sample plots
sample plots
Years
Effect of scale selection on models
Of all the models, including those with the full set of
descriptors and those with reduced variable sets, the
10-ha single-scale models always provided better
results in the AAFCV than either the corresponding
single-scale models with lower resolutions (Table 5)
or the multi-scale models. Therefore, the 10-ha
scale was chosen for variable selection and identification of sites with 'habitat potential'.
Category
--------------------------------------Variable
---------------------------------------
Table 3. Results of least square regression correlation between landscape and forest structure variables. The first row shows the multiple R. The algebraic sign1 of the standardised regression
coefficient b (subsequent rows) indicates whether a variable’s contribution is positive or negative. The number of asterisks indicates the level of significance: * P , 0.05, ** P , 0.01, *** P ,
0.001. b-values . 0.2 are italicised.
tions and forest structure (Table 3). The greatest
influence was recorded for the variables 'days with
snow' (snowd) and 'soil conditions' (scval), which
affect the forest type, and bilberry cover in particular. Among the landscape variable, these two variables were also found to be the best predictors of
capercaillie habitat selection in the ENFA (Table 4).
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
26
Table 4. Contribution of the landscape and land use variables to marginality, specialisation and explained information1 by the
significant first four factors (out of 16) in the 10-ha single scale model. The variables are represented by their code as shown in Table 1.
Positive values in the marginality factor indicate preference and negative values indicate avoidance. Variables are sorted by category
(landscape and land use) and decreasing value of coefficients on the marginality factor. Contributions to marginality, explained
specialisation and explained information . 0.2 are italicised. For the topographic exposure variable (topex), low values indicate high
topographical exposure and high values indicate topographical depressions. Therefore, the minus sign in the coefficient on the
marginality factor indicates preference for exposed sites.
Factor
F1
F2
F3
F4
F1-F4
-------------------- ---------------------- ---------------------- ------------------------ ----------------------------------Marginality
Specialisation
Specialisation
Specialisation
Contribution to explained
(28.9%)
(22.2%)
(19.2%)
(8.9%)
specialisation (79.2%)
F1-F4
---------------------------------Contribution to explained
information (89.6%)
snowd
0.442
-0.077
0.093
-0.013
0.207
0.338
scval
0.310
0.000
0.020
-0.008
0.119
0.226
topex
-0.078
-0.012
-0.001
-0.010
0.033
0.058
slope
0.064
0.016
-0.013
0.014
0.033
0.050
soldur
0.016
0.005
-0.018
0.019
0.014
0.015
solrad
0.003
0.001
0.001
0.006
0.002
0.003
--------------- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------agdist
0.333
0.005
-0.105
0.006
0.149
0.252
stdist
0.320
0.009
0.005
0.000
0.121
0.232
urbdist
0.313
-0.012
0.018
-0.031
0.125
0.230
fcomi
0.303
-0.022
0.001
0.006
0.120
0.222
agall
-0.273
0.626
0.791
-0.552
0.529
0.386
foall
0.270
0.775
0.549
-0.704
0.528
0.384
agfor
-0.228
0.008
-0.220
0.051
0.145
0.191
stall
-0.198
-0.008
-0.032
-0.014
0.098
0.154
sttra
-0.186
0.011
0.031
0.129
0.093
0.145
urb
-0.151
-0.007
0.033
-0.398
0.110
0.133
1
Explained information 5 K (1*contribution to marginality + 0.792 * contribution to specialisation).
atively correlated (r . 0.9). In addition, the results
show a strong avoidance of areas bordering agricultural land (agfor). With regard to linear infrastructure, the distance to roads was more important than
the density of roads, independently of whether traffic density was included or not.
Based on the criteria described, only two landscape variables (snowd and scval) and three land
use pattern variables (foall, stdist and agfor) were
retained for the final model to calculate the indices
of habitat potential. The reasons for omitting the
other variables are given in Table 1.
Landscape 'habitat potential' models
The indices to 'habitat potential' (Fig. 1) were calculated on the basis of the five most important variables. Three significant factors accounted for
94.2% of the explained specialisation and 97.1%
of the explained information. Global marginality
was high (1.145), indicating that capercaillie habitats differed greatly from average conditions in the
study area. Global specialisation (2.601) and tolerance (0.384) indicate a relatively narrow niche
breadth. With only five variables, a large degree
of capercaillie habitat selection at the landscape
Table 5. Results of the area-adjusted k-fold cross-validation for single and multi-scale models with the full descriptor set, and for the
final model (10-ha scale) with the reduced descriptor set. Correlations between modelled 'habitat potential' and area-adjusted frequency of capercaillie presence points are given by the continuous Boyce index (Bcont, 6 SD) and the 'regular' Boyce index (B4bins,
6 SD), both ranging from -1 (wrong model) to 1 (good model). The capacity to predict the spatial pattern of capercaillie distribution is
provided by the absolute validation index (AVI, 6 SD) and the contrast validation index (CVI, 6 SD), both ranging between
0 (random model) and 0.5 (good model). SD 5 standard deviation.
Model type
Full descriptor set
Scale
Bcont
SD
B4bins
SD
AVI
SD
CVI
SD
Single scale_10
10
0.83
0.23
0.82
0.24
0.52
0.20
0.44
0.20
Single scale_100
100
0.58
0.36
0.78
0.27
0.50
0.08
0.38
0.08
Single scale_500
500
0.76
0.27
0.70
0.32
0.52
0.24
0.43
0.23
Single scale_1000
1,000
0.56
0.51
0.64
0.39
0.51
0.29
0.42
0.28
Multi_scale
Multi
0.61
0.43
0.72
0.33
0.52
0.24
0.43
0.24
----------------------------------------------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------Reduced descriptor set (final model)
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Landscape habitat potential (5 variables)
10
0.70
0.44
0.78
0.27
0.52
0.24
0.45
0.24
27
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:53:58
27
Figure 1. Landscape 'habitat potential' for capercaillie in the Black Forest, Germany, before considering patch size and connectivity
(A), compared with current capercaillie distribution mapped in 2003 (B).
scale could be explained. The reduction in number of variables led to only a moderate decrease in
model quality compared to the full descriptor set,
which was reflected by a decrease in the Boyce indices and an increase in the standard deviation (see
Table 5).
Indices of 'habitat potential' vs
capercaillie distribution
Although the distribution pattern of the 'habitat potential' in the study area closely matched the areas of
actual capercaillie presence (see Fig. 1), great differences between the three subregions became apparent (Fig. 2) when comparing the 'habitat potential'
values within the capercaillie areas mapped in 2003.
Figure 2. Distribution of the landscape 'habitat potential' in the
areas currently inhabited by capercaillie (mapped in 2003), differentiated according to the different subregions (%: southern;
&: northern; &: eastern) of the Black Forest, Germany.
28
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:53:59
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
28
Discussion
Modelling approach
In view of the increasing conflict between human
activities and the need to protect habitats of endangered species, concepts are required for optimising
the use of limited spatial and financial resources.
Therefore, predictive species-habitat models have
become a popular planning instrument. However,
as they are based on comparisons of the observed
current distribution of a species and the current
state of its habitat, the models are by default static
and assume an equilibrium between environment
and species distribution (Guisan & Zimmermann
2000). Such equilibrium, however, seldom corresponds to reality (Pickett et al. 1994). Natural dynamics and continuously changing anthropogenic
land use, as well as far reaching effects of historical
processes, continue to affect the spatial patterns of
animal-habitat relationships. Yet, the incorporation of such processes in dynamic simulation models is very difficult (e.g. Lischke et al. 1998, Guisan
& Zimmermann 2000) and frequently unrealisable
in the context of applied nature conservation issues.
Our approach was, therefore, to identify areas with
a long-term potential for producing and maintaining suitable habitat for capercaillie by exhausting
the possibilities of a static resource selection model.
To minimise the aforementioned problems of static
models we considered three aspects: First, the sample locations were restricted to areas with stable
subpopulations. Second, we focused on comparatively stable indirect predictor variables for habitat
quality, and third, an ENFA, requiring only presence data, was preferred over a logistic regression
model to reduce the influence of 'false' absence data.
Such false data may arise from anthropogenic impacts producing unsuitable habitat structures at
a local scale in areas where the prevailing landscape
conditions are in fact suitable. The applicability and
transferability of the methods to other species and
regions was another important criteria for choosing
the ENFA approach, as reliable absence data are
often unavailable in practical conservation management.
Influence of landscape and land use variables on
capercaillie habitat selection
The landscape descriptors identified as being important for capercaillie in the Black Forest resembled those of other investigations (Suchant 2002,
Sachot 2002, Graf et al. 2005). Therefore, we will
only discuss deviations from these results and new
findings in this paper.
Our study is the first to examine the explanatory
power of different elevation-correlated climate
variables. The variable 'number of days with
. 10 cm snow cover' was found to perform slightly
better than the other two altitude-correlated variables, vegdur and tempyear. This may be due to
areas with a longer duration of snow cover being
more susceptible to snow damage, which may promote the creation of open and structured habitat
conditions. Furthermore, snow cover may also reduce predator population densities in winter and
enhance the possibilities for saving energy by resting in snow 'igloos'.
The influence of soil conditions on capercaillie
habitats has received little attention, despite the fact
that they impact the species composition of trees
and ground vegetation, as well as the growth rate
of plants, especially bilberry, which is a key source
of food for capercaillie (e.g. Storch 1993a). Graf et
al. (2005) integrated only sites with extreme soil
conditions (moors and wet forests) into their model,
whereas we used a graded weighting of soil conditions for the first time.
Forest fragmentation is reportedly linked to a reduction in reproductive success because of increased predation in areas bordering agricultural
land (e.g. Andrén & Angelstam 1988, Kurki et al.
2000, Storch et al. 2005). In our study capercaillie
apparently avoided such border areas. However, we
could have underestimated the possible positive influence of small, non-intensively used open land
areas, as we considered neither the size of the agricultural areas nor the distinction between intensive
and non-intensive land use.
Impact of spatial scale
As different environmental variables impact species
at different spatial scales (Freemark & Merriam
1986, Levin 1992), multi-scale approaches are becoming increasingly common (Bissonette 1997).
Graf et al. (2005) found that better model results
can be obtained for a generalised linear model when
the variables are integrated at the scale at which they
make the highest contribution to the explained variance in univariate models. In the case of an ENFA,
such a selection of variables can be difficult, as their
contribution to the explained information depends
on the other variables integrated into the model.
Two aspects must be distinguished when determining the 'optimal' scale: 1) the influence of scale
29
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:54:08
29
on the explanatory power of individual variables
for habitat selection and 2) the influence of the selected variable scales on the overall quality of the
model results. Hirzel et al. (2004) quantified the
performance of the same variables on different spatial scales in the context of an ENFA for the bearded vulture Gypaetus barbatus. They found that in
the case of generalised variables quantifying the
availability of a resource within a predefined area,
the contribution to marginality and specialisation
mostly increased with spatial scale. Zajec et al.
(2005) compared the results of single-scale ENFA
models comprising the same set of variables, but
with different spatial resolutions, and found that
model quality decreased with an increase in scale.
Both, seemingly contradictory phenomena, also
apply in our investigation. However, a multi-scale
model containing all variables at the scale where
they performed best failed to provide better results
than the 10-ha single scale model. As an analysis of
landscape-scale patterns by simply calculating the
mean availability of a resource within a circular
moving window implies a high degree of generalisation, it may lead to misclassification of small unsuitable landscape structures (e.g. roads, small settlements and arable fields). Consequently the
predictive power of a model containing such highly
generalised variables is low.
Nevertheless, in view of the spatial requirements
of capercaillie, the availability and distribution of
'habitat potential' at the landscape scale cannot be
ignored. Therefore, we would suggest a two-step
approach. First, select, as we did, the high-resolution 10-ha single-scale model for the quantification
of the 'habitat potential'. Second, to select priority
sites for practical habitat management, one should
retain only those patches with 'habitat potential'
that are large enough to be inhabited by capercaillie, and which theoretically can be reached by the
birds. Although we do not present the results here,
we tested this procedure by selecting only patches of
'habitat potential' at least 100 ha in size, including
smaller ones only if within 100 m of patches of
$ 100 ha. This 100-m threshold was chosen after
an evaluation of all currently inhabited patches.
Furthermore we excluded all uninhabited patches
. 10 km from inhabited patches, because 10 km
corresponds to the mean dispersal distance of capercaillie (Myrberget 1978, Rolstad et al. 1988, Ménoni 1991, Swenson 1991, Storch 1993b, cited in
Storch & Segelbacher 2000). This procedure improved the predictive power of the model and had
the following advantages: 1) a high degree of accuracy is achieved, which improves applicability, and
2) locations with high potential but which are too
small to serve as habitat can be considered for subsequent biotope network models.
Model results and consequences for management
Our results support earlier findings suggesting that
with a small number of landscape parameters most
of the capercaillie distribution can be predicted (Suchant 2002, Graf et al. 2005). Most habitats in the
Black Forest with high indices of 'habitat potential'
are currently occupied by capercaillie. There are,
however, evident differences between the subregions. In the southern part, the 'habitat potential'
is generally lower and highly fragmented, and capercaillie are found in both high- and low-potential
sites. Nevertheless, especially in this region, the spatial pattern of population decline coincides with the
spatial distribution of lacking or low 'habitat potential' (V. Braunisch & R. Suchant, unpubl. data). In
contrast, in the northern part, where the capercaillie
distribution is mainly restricted to high potential
habitats, the population has remained more or less
stable over the last 20 years (Braunisch & Suchant
2006).
Given that in sites with high indices of 'habitat
potential' the prevailing landscape conditions favour the development of suitable habitat, concentrating habitat improvement measures on these
sites leads not only to an ecological but also an
economical optimisation of conservation efforts.
Optimisation will be achieved particularly in areas
where forest management has created unsuitable
habitats. The graduated series of 'habitat potential'
indices simplifies the selection of priority areas, particularly when trying to satisfy predefined area requirements, e.g. for a minimum viable population
(Hovestadt et al. 1992). The model also provides
location-specific information for the location of
protected areas (especially Natura 2000) and the
spatial planning of conservation measures and
land-use activities (especially silviculture, tourism
and wind energy). However, the model could be
improved by considering in greater detail the functional connectivity between patches and the longterm changes in landscape conditions (e.g. climate
change and nitrification).
Acknowledgements - we wish to thank Dr. Alexandre
Hirzel for his helpful advice and his willingness to incorporate new ideas into the BIOMAPPER software, Dr.
Gerald Kändler for the provision of the BWI data and
30
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:54:08
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
30
statistics advice, Dr. Christoph Schneider and Johannes
Schönbein for making available the algorithms for the
calculation of the climate model, Dr. Martin Wiebel for
supporting us with the evaluation of soil conditions, and
Jürgen Kayser for help with the soil condition database.
Last but not least, we would like to thank the numerous
local foresters, hunters and ornithologists who provided
us with data for the monitoring database. The study was
financed by the Baden-Württemberg Fund for Environmental Research.
References
Andrén, H. & Angelstam, P. 1988: Elevated predation
rates as an edge effect in habitat islands: experimental
evidence. - Ecology 69: 544-547.
Austin, M. 2002: Case studies of the case of environmental gradients in vegetation and fauna modeling: Theory
and Practice in Australia and New Zealand. - In: Scott,
F., Heglund, P., Morrison, M., Haufler, J., Raphael,
M., Wall, W. & Samson, F. (Eds.); Predicting species
occurrence: issues of accuracy and scale. Island Press,
Covelo, CA, pp. 73-82.
Austin, M.P. & Smith, T.M. 1989: A new model for the
continuum concept. - Vegetatio 83: 35-47.
Bissonette, J.A. 1997: Scale-sensitive ecological properties:
historical context, current meaning. - In: Bissonette,
J.A. (Ed.); Wildlife and landscape ecology: effects of
pattern and scale. Springer Verlag, New York, pp. 3-31.
Boag, D.A. & Rolstad, J. 1991: Aims and methods of
managing forests for the conservation of tetraonids.
- Ornis Scandinavica 22: 225-226.
Böhner, J., Köthe, R. & Trachinow, C. 1997: Weiterentwicklung der automatischen Reliefanalyse auf der Basis von digitalen Geländemodellen. - Göttinger Geographische Arbeiten 100: 3-21. (In German).
Box, G.E.P. & Cox, D.R. 1964: An analysis of transformation. - Royal Statistics 26: 211-243.
Boyce, M.S., Vernier, P.R., Nielsen, S.E. & Schmiegelow,
F.K.A. 2002: Evaluating resource selection functions.
- Ecological Modelling 157: 281-300.
Braunisch, V., Bollmann, K., Graf, R.F. & Hirzel, A.H.
submitted: Living on the edge - modelling habitat suitability for species at the edge of their fundamental
niche. - Ecological Modelling.
Braunisch, V. & Suchant, R. 2006: Das RaufußhühnerBestandesmonitoring der FVA. - Berichte Freiburger
Forstliche Forschung 64: 55-67. (In German).
Cas, J. & Adamic, M. 1998: The influence of forest alteration on the distribution of capercaillie leks in the Eastern Alps. - Zbornik 57: 5-57.
ESRI 1996: Using ArcView GIS: User manual. - Environmental Systems Research Institute, Redlands, California, 350 pp.
Fielding, A.H. & Bell, J.F. 1997: A review of methods for
the assessment of prediction errors in conservation
presence/absence models. - Environmental Conservation 24: 38-49.
Freemark, K.E. & Merriam, H.G. 1986: Importance of
area and habitat heterogeneity to bird assemblages in
temperate forest fragments. - Biological Conservation
36: 115-141.
Gatter, W. 2004: Deutschlands Wälder und ihre Vogelgesellschaften im Rahmen von Gesellschaftswandel
und Umwelteinflüssen. (In German with an English
summary: German forests and their avian communities
in the context of changes in society and environment).
- Vogelwelt 125: 151-176.
Gjerde, I., Rolstad, J., Wegge, P. & Larsen, B.B. 1989:
Capercaillie leks in fragmented forests: a 10-year study
of the Torstimäki population, Varaldskogen. - Transactions Congress International Union Game Biologist
Trondheim, Norway 19: 454-459.
Graf, R.F., Bollmann, K., Suter, W. & Bugmann, H.
2005: The importance of spatial scale in habitat models: capercaillie in the Swiss Alps. - Landscape Ecology
20: 703-717.
Guisan, A., Weiss, S.B. & Weiss, A.D. 1999: GLM versus
CCA spatial modeling of plant species distribution.
- Plant Ecology 143: 107-122.
Guisan, A. & Zimmermann, N.E. 2000: Predictive habitat distribution modes in ecology. - Ecological Modelling 135: 147-186.
Hannah, P., Palutikof, J.P. & Quine, C.P. 1995: Predicting windspeeds for forest areas in complex terrain. - In:
Coutts, M.P. & Grace, J. (Eds.); Wind and trees. Cambridge University Press, Edinburgh, U.K., pp. 113-132.
Helle, P., Jokimäki, J. & Lindén, H. 1990: Habitat selection of the male capercaillie in northern Finland:
a study based on radio-telemetry. - Suomen Riista 36:
72-81.
Hirzel, A.H. & Arlettaz, R. 2003a: Environmental-envelope based habitat-suitability models. - In: Manly,
B.F.J. (Ed.); 1st Conference on Resource Selection by
Animals. Omnipress, Laramie, USA, pp. 67-76.
Hirzel, A.H. & Arlettaz, R. 2003b: Modelling habitat
suitability for complex species distributions by the environmental-distance geometric mean. - Environmental Management 32: 614-623.
Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. 2002:
Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? - Ecology
83: 2027-2036.
Hirzel, A.H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. 2006: Evaluating the ability of habitat suitability models to predict species presences. - Ecological
Modelling 199: 142-152.
Hirzel, A.H., Posse, B., Oggier, P.A., Crettenand, Y.,
Glenz, C. & Arlettaz, R. 2004: Ecological requirements
of reintroduced species and the implications for release
policy: the case of the bearded vulture. - Journal of
Applied Ecology 41: 1103-1116.
31
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:54:08
31
Hovestadt, T., Roeseer, J. & Mühlenberg, M. 1992: Flächenbedarf von Tierpopulationen. - Berichte aus ökologischer Forschung. Forschungszentrum Jülich, 1,
277 pp. (In German).
Hutchinson, G.E. 1957: Concluding remarks. - Cold
Spring Harbour Symposium on Quantitative Biology
22: 415-427.
Klaus, S., Andreev, V., Bergmann, H.H., Müller, F., Porkert, J. & Wiesner, J. 1989: Die Auerhühner. - Die Neue
Brehm-Bücherei. Volume. 86. Westarp Wissenschaften, Magdeburg, 276 pp. (In German).
Kobler, A. & Adamic, M. 2000: Identifying brown bear
habitat by a combined GIS and machine learning
method. - Ecological Modelling 135: 291-300.
Kurki, S., Nikula, A., Helle, P. & Linden, H. 2000: Landscape fragmentation and forest composition effects on
grouse breeding success in boreal forests. - Ecology 81:
1985-1997.
Levin, S.A. 1992: The problem of pattern and scale in
ecology. - Ecology 73: 1943-1967.
Lischke, H., Guisan, A., Fischlin, A. & Bugmann, H.
1998: Vegetation responses to climate change in the
Alps - Modelling studies. - In: Cebon, P., Dahinden,
U., Davies, H., Imboden, D. & Jaeger, C. (Eds.); A view
from the Alps: Regional Perspectives on climate
Change. MIT Press, Boston, pp. 309-350.
MacArthur, R.H. 1960: On the relative abundance of
species. - American Naturalist 94: 25-36.
Ménoni, E. 1991: Ecologie et dynamique des population
du grand tetras dans les Pyrénées, avec des reference
speciales à la biologie de la réproduction chez les poules
- quelques applications à sa conservation. - Doctoral
thesis Université P. Sabatier, Toulouse, France, 401
pp.
Ménoni, E. 1994: Statut, évolution et facteurs limitants
des populations françaises de grand tétras Tetrao urogallus: synthèse bibliographique. (In French with an
English summary: Status, trends and limiting factors
of capercaillie Tetrao urogallus in France: a literature
review.) - Gibier Faune Sauvage Game and Wildlife
11(1): 97-158.
Mitchell, S.J., Hailemariam, T. & Kulis, Y. 2001: Empirical modeling of cutblock edge windthrow risk on Vancouver Island, Canada, using stand level information.
- Forest Ecology and Management 154: 117-130.
Mladenoff, D.J. & Sickley, T.A. 1998: Assessing potential gray wolf restoration in the northeastern United
States: A spatial prediction of favourable habitat and
potential population levels. - Journal of Wildlife Management 62: 1-10.
Myrberget, S. 1978: Vandringer og alderfordeling hos
orrfugl og sturfugl i Skandinavia. - Var Fuglfauna 1:
69-75.
Pickett, S.T.A., Kolasa, G. & Jones, C.G. 1994: Ecological understanding: the nature of Theory and the Theory of Nature. - Academic Press, New York, 228 pp.
Rolstad, J. 1989: Habitat and range use of capercaillie
Tetrao urogallus L. in southcentral Scandinavian boreal forests, with special reference to the influence of
modern forestry. - PhD thesis, Department of Nature
Conservation, Agricultural University of Norway, AsNLH, Norway, 109 pp.
Rolstad, J., Wegge, P. & Larsen, B.B. 1988: Spacing and
habitat use of capercaillie during summer. - Canadian
Journal of Zoology 66: 670-679.
Sachot, S. 2002: Viability and management of an endangered capercaillie (Tetrao urogallus) metapopulation. Doctoral thesis, Faculté des Sciences de l’Université de
Lausanne, Lausanne 2002, 117 pp.
Schadt, S., Revilla, E., Wiegand, T., Knauer, F., Kaczenski, P., Breitenmoser, U., Bufka, L., Cerveny, J.,
Koubek, P., Huber, T., Stanisa, C. & Trepl, L. 2002:
Assessing the suitability of central European landscapes for the reintroduction of the Eurasian lynx.
- Journal of Applied Ecology 39(2): 189-203.
Scherzinger, W. 1989: Biotopansprüche bedrohter Waldvogelarten und ihre Eingliederung in die Waldsukzession. - Stapfia/Linz 20: 81-100. (In German).
Scherzinger, W. 1991: Das Mosaik-Zyklus-Konzept aus
der Sicht des zoologischen Artenschutzes. - In: Bayerische Akademie für Naturschutz und Landschaftspflege (Ed.); Das Mosaik-Zyklus-Konzept und seine
Bedeutung für den Naturschutz. Laufener Seminarbeiträge 5/91, pp. 30-42. (In German).
Schneider, C. & Schönbein, J. 2003: Klimatologische Analyse der Schneesicherheit und Beschneibarkeit von
Wintersportgebieten in deutschen Mittelgebirgen.
- Gutachten für die Stiftung Sicherheit im Skisport
des Deutschen Skiverbandes, 97 pp. (In German).
Schroth, K.E. 1995: Lebensräume des Auerhuhns im
Nordschwarzwald: dargestellt am Beispiel der Kaltenbronner Wälder. - Naturschutzreport 10: 27-46. (In
German).
Simberloff, D. 1998: Flagships, umbrellas and keystones:
is single-species management passé in the landscape
area? - Biological Conservation 83(3): 247-257.
Sokal, R.R. & Rohlf, F.J. 1981: Biometry: The principles
and practice of statistics in biological research. - W.H.
Freeman and Co., New York, 887 pp.
StatSoft Inc. 1999: STATISTICA for Windows. Edition
'99. - StatSoft, Inc., 2300 East 14th Street, Tulsa, OK
74104, USA, 321 pp.
Storch, I. 1993a: Habitat selection by capercaillie in summer and autumn - is bilberry important? - Oecologia
95: 257-265.
Storch, I. 1993b: Habitat use and spacing of capercaillie
in relation to forest fragmentation patterns. - Dissertation, Universität München Fakultät für Biologie, 97
pp.
Storch, I. 1995: Habitat requirements of capercaillie.
- Proceedings of the 6th International Grouse Symposium, pp. 151-154.
32
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:54:08
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
32
Storch, I. 2000: Status survey and Conservation Action
Plan 2000-2004: Grouse. - WPA/BirdLife/SSC Grouse
Specialist Group. IUCN, Gland, Switzerland and
Cambridge, UK and World Pheasant Association,
Reading, UK, 112 pp.
Storch, I. 2002: On spatial resolution in habitat models:
Can small-scale forest structure explain capercaillie
numbers? - Conservation Ecology 6, Available at:
http://www.consecol.org/vol6/iss1/art6
Storch, I. & Segelbacher, G. 2000: Genetic correlates of
spatial population structure in central European capercaillie Tetrao urogallus and black grouse T. tetrix:
a project in progress. - Wildlife Biology 6: 239-243.
Storch, I., Woitke, E. & Krieger, S. 2005: Landscapescale edge effect in predation risk in forest-farmland
mosaics of central Europe. - Landscape Ecology 20:
927-940.
Suchant, R. 2002: Die Entwicklung eines mehrdimensionalen Habitatmodells für Auerhuhnareale (Tetrao urogallus L.) als Grundlage für die Integration von
Diversität in die Waldbaupraxis. - Schriftenreihe
Freiburger Forstliche Forschung 16: 331.
Suchant, R. & Braunisch, V. 2004: Multidimensional
habitat modelling in forest management - a case study
using capercaillie in the Black Forest, Germany. - Ecological Bulletins 51: 455-649.
Suter, W., Graf, R.F. & Hess, R. 2002: Capercaillie (Tetrao
urogallus) and avian biodiversity: testing the umbrellaspecies concept. - Conservation Biology 16: 778-788.
Swenson, J.E. 1991: Is the hazel grouse a poor disperser?
- Transactions International Union of Game Biologists
20: 347-352.
Wegge, P., Rolstad, J. & Gjerde, I. 1995: Effects of boreal
forest fragmentation of Capercaillie Grouse: Empirical
evidence and management implications. - In: McCulloth, D.R. & Barrett, R.H. (Eds.); Wildlife 2001: Populations, pp. 738-749.
Wilson, J.D. 1984: Determining a topex score. - Scottish
Forestry 38(4): 251-256.
Zajec, P., Zimmermann, F., Roth, H.U. & Breitenmoser,
U. 2005: Die Rückkehr des Bären in die Schweiz - Potentielle Verbreitung, Einwanderungsrouten und mögliche Konflikte. - KORA Bericht Nr. 28, 31 pp. (In
German).
33
E WILDLIFE BIOLOGY ? 13:Suppl. 1 (2007)
Wildlife Biology wbio-13-01s1-13.3d 4/5/07 19:54:09
33